Code covered by the BSD License

# Differential Search Algorithm: A modernized particle swarm optimization algorithm

### PINAR CIVICIOGLU (view profile)

06 Sep 2013 (Updated )

DSA is a modernized particle swarm optimization algorithm.

ds(method,fnc,mydata,size_of_superorganism,size_of_one_clan,low_habitat_limit,up_habitat_limit,epoch)
```%
%
% DIFFERENTIAL SEARCH ALGORITHM (DSA) (in MATLAB)
% STANDARD VERSION of DSA (16.July.2013)
%
%
% usage : > ds(method,fnc,mydata,popsize,dim,low,up,maxcycle)
%
% method
%--------------------------------------------------------------
% 1: Bijective DSA (B-DSA)
% 2: Surjective DSA (S-DSA)
% 3: Elitist DSA (strategy 1) (E1-DSA)
% 4: Elitist DSA (strategy 2) (E2-DSA)
% if method=[. . . ...],   Hybrid-DSA (H-DSA)
%--------------------------------------------------------------
% example :
% ds(1,'circlefit',mydata,10,3,-10,10,2000) ; % B-DSA
% ds(2,'circlefit',mydata,10,3,-10,10,2000) ; % S-DSA
% ds(3,'circlefit',mydata,10,3,-10,10,2000) ; % E1-DSA
% ds(4,'circlefit',mydata,10,3,-10,10,2000) ; % E2-DSA
% ds([1 2],'circlefit',mydata,10,3,-10,10,2000) ; % Hybrid-DSA, in this case B-DSA and S-DSA are hybridized.
%--------------------------------------------------------------
% P.Civicioglu, "Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm",  Computers & Geosciences, 46 (2012), 229-247.
% P.Civicioglu, "Understanding the nature of evolutionary search algorithms", Additional technical report for the project of 110Y309-Tubitak,2013, Ankara, Turkey.
%
%
%
%
%--------------------------------------------------------------
%{
19.March.2013
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
%}

function ds(method,fnc,mydata,size_of_superorganism,size_of_one_clan,low_habitat_limit,up_habitat_limit,epoch)

% size_of_superorganism ; size of population.
% size_of_one_clan ; size of problem dimension (1,2,3,...,d), where each clan (i.e. sub-superorganism) includes d-individuals.
% mydata ; additional parameters for objective function, use mydata=[], if it is not needed. See Best-Fit Circle example (circlefit.m) for usage of mydata.

%Initialization

% control of habitat limits
if numel(low_habitat_limit)==1,
low_habitat_limit=low_habitat_limit*ones(1,size_of_one_clan);
up_habitat_limit=up_habitat_limit*ones(1,size_of_one_clan);
end

% generate initial individuals, clans and superorganism.
superorganism=genpop(size_of_superorganism,size_of_one_clan,low_habitat_limit,up_habitat_limit);
% success of clans/superorganism
fit_superorganism=feval(fnc,superorganism,mydata);

for epk=1:epoch

% SETTING OF ALGORITHMIC CONTROL PARAMETERS
% Trial-pattern generation strategy for morphogenesis; 'one-by-one morphogenesis'.
% p1=0.0*rand;  % i.e.,  0.0 <= p1 <= 0.0
% p2=0.0*rand;  % i.e.,  0.0 <= p2 <= 0.0

% Trial-pattern generation strategy for morphogenesis; 'one-or-more morphogenesis'. (DEFAULT)
p1=0.3*rand;  % i.e.,  0.0 <= p1 <= 0.3
p2=0.3*rand;  % i.e.,  0.0 <= p2 <= 0.3

%-------------------------------------------------------------------

[direction,msg]=generate_direction(method(randi(numel(method))),superorganism,size_of_superorganism,fit_superorganism);

map=generate_map_of_active_individuals(size_of_superorganism,size_of_one_clan,p1,p2);

%-------------------------------------------------------------------
% Recommended Methods for generation of Scale-Factor; R
% R=4*randn;  % brownian walk
% R=4*randg;  % brownian walk
% R=lognrnd(rand,5*rand);  % brownian walk
R=1./gamrnd(1,0.5);   % pseudo-stable walk
% R=1/normrnd(0,5);    % pseudo-stable walk

%-------------------------------------------------------------------

% bio-interaction (morphogenesis)
stopover=superorganism+(R.*map).*(direction-superorganism);

% Boundary Control
stopover=update(stopover,low_habitat_limit,up_habitat_limit);

% Selection-II
fit_stopover=feval(fnc,stopover,mydata);
ind=fit_stopover<fit_superorganism;
fit_superorganism(ind)=fit_stopover(ind);
superorganism(ind,:)=stopover(ind,:);

% update results
[globalminimum,indexbest]=min(fit_superorganism);
globalminimizer=superorganism(indexbest,:);

% export results
assignin('base','globalminimum',globalminimum);
assignin('base','globalminimizer',globalminimizer);
fprintf('%s  | %5.0f   --->   %10.16f\n',msg,epk,globalminimum)

end

function pop=genpop(a,b,low,up)
pop=ones(a,b);
for i=1:a
for j=1:b
pop(i,j)=rand*(up(j)-low(j))+low(j);
end
end

function p=update(p,low,up)
[popsize,dim]=size(p);
for i=1:popsize
for j=1:dim
% first (standard)-method
if p(i,j)<low(j), if rand<rand, p(i,j)=rand*(up(j)-low(j))+low(j); else p(i,j)=low(j); end, end
if p(i,j)>up(j),  if rand<rand, p(i,j)=rand*(up(j)-low(j))+low(j); else p(i,j)=up(j); end, end

%{
%  second-method
if rand<rand,
if p(i,j)<low(j) || p(i,j)>up(j), p(i,j)=rand*(up(j)-low(j))+low(j); end
else
if p(i,j)<low(j), p(i,j)=low(j); end
if p(i,j)>up(j),  p(i,j)=up(j); end
end
%}
end
end

function [direction,msg]=generate_direction(method,superorganism,size_of_superorganism,fit_superorganism);
switch method
case 1,
% BIJECTIVE DSA  (B-DSA) (i.e., go-to-rnd DSA);
% philosophy: evolve the superorganism (i.e.,population) towards to "permuted-superorganism (i.e., random directions)"
direction=superorganism(randperm(size_of_superorganism),:); msg=' B-DSA';
case 2,
% SURJECTIVE DSA (S-DSA) (i.e., go-to-good DSA)
% philosophy: evolve the superorganism (i.e.,population) towards to "some of the random top-best" solutions
ind=ones(size_of_superorganism,1);
[null_,B]=sort(fit_superorganism);
for i=1:size_of_superorganism, ind(i)=B(randi(ceil(rand*size_of_superorganism),1)); end;
direction=superorganism(ind,:);  msg=' S-DSA';
case 3,
% ELITIST DSA #1 (E1-DSA) (i.e., go-to-best DSA)
% philosophy: evolve the superorganism (i.e.,population) towards to "one of the random top-best" solution
[null,jind]=sort(fit_superorganism); ibest=jind(ceil(rand*size_of_superorganism)); msg='E1-DSA';
direction=repmat(superorganism(ibest,:),[size_of_superorganism 1]);
case 4,
% ELITIST DSA #2 (E2-DSA) (i.e., go-to-best DSA)
% philosophy: evolve the superorganism (i.e.,population) towards to "the best" solution
[null_,ibest]=min(fit_superorganism); msg='E2-DSA';
direction=repmat(superorganism(ibest,:),[size_of_superorganism 1]);
end
return

function map=generate_map_of_active_individuals(size_of_superorganism,size_of_one_clan,p1,p2);
% strategy-selection of active/passive individuals
map=zeros(size_of_superorganism,size_of_one_clan);
if rand<rand,
if rand<p1,
% Random-mutation #1 strategy
for i=1:size_of_superorganism
map(i,:)=rand(1,size_of_one_clan) < rand;
end
else
% Differential-mutation strategy
for i=1:size_of_superorganism
map(i,randi(size_of_one_clan))=1;
end
end
else
% Random-mutation #2 strategy
for i=1:size_of_superorganism
map(i,randi(size_of_one_clan,1,ceil(p2*size_of_one_clan)))=1;
end
end
return```